Natural Language Processing Based Part of Speech Tagger using Hidden Markov Model

Nambiar, Sindhya K; Leons, Antony; Jose, Soniya; Arunsree,

doi:10.1109/i-smac47947.2019.9032593

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Hidden Markov model plays an important role in natural language processing (NLP) for PART-OF-SPEECH (POS) tagging. Nambiar et al built a Hidden Markov model by using the existing marker sentences in Malayan language [Nambiar, Leons, Jose et al (2019)]. The Viterbi algorithm based on the HMM model predicts the optimal hidden state sequence satisfying the observation sequence by giving the known observation sequence and model parameters.…”

Section: Data Cleaningmentioning

confidence: 99%

COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

Zhang¹,

Chen²,

Liu³

et al. 2020

Computers, Materials &Amp; Continua

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With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the depth of the seven emotions such as Hopeful, Happy, and Depressed. Finally, we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model. The results show that our model has better generalization ability and smaller discriminant error. We designed a large data visualization screen, which can clearly show the trend of public emotions, the proportion of various emotion categories, keywords, hot topics, etc., and fully and intuitively reflect the development of public opinion.

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Section: Data Cleaningmentioning

confidence: 99%